Competitive Advantage through Big Data Analytics

Transcription

1 Competitive Advantage through Big Data Analytics Thesis by: Usarat Thirathon A Thesis Submitted in Fulfilment of the Requirements for the Degree of Doctor of Philosophy 2017 Supervisory Panel: Associate Professor Bernhard Wieder Professor Zoltan Matolcsy Doctor Maria-Luise Ossimitz Accounting Discipline Group University of Technology Sydney

2 CERTIFICATE OF ORIGINAL AUTHORSHIP I certify that the work in this thesis has not previously been submitted for a degree nor has it been submitted as part of requirements for a degree except as part of the collaborative doctoral degree and/or fully acknowledged within the text. I also certify that the thesis has been written by me. Any help that I have received in my research work and the preparation of the thesis itself has been acknowledged. In addition, I certify that all information sources and literature used are indicated in the thesis. Usarat Thirathon 9 November 2017 i

3 ACKNOWLEDGEMENTS This thesis and my PhD journey could not be completed without the contribution of a number of people. First, I would like to express my sincere gratitude and appreciation to my principal supervisor, Associate Professor Bernhard Wieder, for his believe in me since the beginning of my PhD. Bernhard s continuous support, guidance, immense knowledge, motivation, and patience helped me go through the whole processes of fine tuning my research interest, developing solid research questions, sharpening my research knowledge, and finally, writing and finishing this thesis. He was with me to congratulate me during good times, and to support and even help resolve any problem I encountered. As I have always said, he is the best supervisor I could have imagined. Besides, I would like to thank the rest of my supervisory panel: Professor Zoltan Matolcsy and Dr. Maria-Luise Ossimitz, for their insightful comments and encouragement. I also would like to acknowledge the support from the UTS Accounting Discipline Group, in particularly Professor Martin Bugeja, Dr. Peter Chapman, Judith Evans, Dr. Rachael Lewis, Ross McClure, Dr. Hannah Pham, Katt Robertson, Neil James and Shona Bates. I would like to also thank to Accounting Department, Faculty of Business Administration, Kasetsart University, for giving me a chance and support to pursue my doctoral study. My PhD study could not even commence without support from Associate Professor Sasivimol Meeampol, Associate Professor Paiboon Pajongwong, Dr. Suneerat Wuttichindanon, Dr. Jarupa Vipoopinyo and Dr. Phorntep Rattanataipop. ii

4 PhD life could not complete without friends. I would like to thank Kamonwan Sattayayut (for introducing me to a group of people whom later became good friends of mine), Pattanapong Chantamit-o-pas (for introducing me to many lighthouses in Australia), Piyachat Leelasilapasart (for being a sister, a photographer, an assistant, and a good listener), as well as other Thai PhD students (Ping, Neu, Van, Kwang, Good, On, Gam, and Bua for good times out, food, and trips). Most importantly, I would give special thanks to my parents, Yanee and Virayuth Thirathon, for their unconditional love and support. For a mother of two children to pursue a PhD in another country, many sacrifices were required not only by myself, but also by my parents. For this reason, I would like to dedicate my PhD to my mum and dad. I also would like to thank my brother, Nattavude Thirathon, for being there whenever and for whatever reason I need him. Last, but not least, I would like to acknowledge my children: Pat and Pete. Without their smiles, I would not have had enough strength to go through the PhD. Thank you for being a good girl and boy. I am so proud of them and wish that they are proud of me too. iii

5 TABLE OF CONTENTS Certificate of Original Authorship...i Acknowledgements...ii Table of Contents...iv List of Tables...vi List of Figures...vii List of Abbreviations...viii Abstract...ix CHAPTER 1 - INTRODUCTION Research Background Research Questions Objectives Motivation Research Method Results and Key Findings Contributions Thesis Structure... 9 CHAPTER 2 LITERATURE REVIEW Big Data Big Data Analytics (BDA) Analytics-based Decision-Making (ABDM) Organisational Analytic Culture Conclusion CHAPTER 3 - HYPOTHESES DEVELOPMENT Big Data Analytics for Competitive Advantage Big Data Analytics and Competitive Advantage, mediated by Analytics- Based Decision-Making Moderating Effect of Big Data iv

6 3.4 Organisational Analytic Culture, Big Data Analytics, and Competitive Advantage Organisational Analytic Culture, Analytics-Based Decision-Making, and Competitive Advantage Conclusion CHAPTER 4 RESEARCH METHOD Data Collection Method Questionnaire Development Construct Measurement Selection of Target Respondents and Survey Delivery Summary CHAPTER 5 DATA ANALYSIS AND RESULTS Survey Response Analysis of Data Characteristics and Data Quality Partial Least Squares Analysis Hypotheses Testing Model Fit Summary CHAPTER 6 - CONCLUSION Summary of Results Theoretical Implications Practical Implications Research Limitations Future Research Opportunities Appendix A Appendix B Appendix C REFERENCES v

7 LIST OF TABLES Table 1 Indicators for Analytic Tools Table 2 Indicators for Analytic Methods Table 3 Indicators for Big Data Table 4 Indicators for Analytics-Based Decision-Making Table 5 Indicators for Organisational Analytic Culture Table 6 Indicators of Competitive Advantage Table 7 Exclusion of Contacts Table 8 Survey Responses Table 9 Industry Response Table 10 Organisational Size Table 11 Position Name Table 12 Time in Organisation and Position Table 13 Measures of Validity and Reliability Table 14 Fornell Larcker Criterion for Discriminant Validity (First Order) Table 15 HTMT Values for Discriminant Validity (First Order) Table 16 Fornell Larcker Criterion for Discriminant Validity Table 17 HTMT Values for Discriminant Validity Table 18 Collinearity Statistics Table 19 Hypotheses Testing Table 20 Model Fit Table 21 Indicator and Construct Descriptive Statistics Table 22 Shapiro-Wilk and Kolmogorov-Smirnov Test Table 23 Harman's Single Factor Test Table 24 Independent Sample Test Table 25 Outer Loadings Table 26 Cross Loadings vi

8 LIST OF FIGURES Figure 1 Hypothesis Figure 2 Hypothesis Figure 3 Hypothesis Figure 4 Hypothesis Figure 5 Hypothesis Figure 6 Research Model Figure 7 Formative Second Order Constructs and Weighting Values Figure 8 Structural Model 1a Figure 9 Structural Model 1b Figure 10 Structural Model vii

9 LIST OF ABBREVIATIONS ABDM BDA BI BI&A DSS ERP FTE IS OAC PLS PLS-SEM RBT Analytics-Based Decision-Making Big Data Analytics Business Intelligence Business Intelligence and Analytics Decision Support Systems Enterprise Resource Planning Full-Time Equivalent Information Systems Organisational Analytic Culture Partial Least Squares Partial Least Squares Structural Equation Modelling Resource-Based Theory viii

10 ABSTRACT Big Data has become a major topic of interest and discussion for both academics and professionals in the IT and business disciplines, and evidence from case studies suggests that companies which have invested in Big Data outperform others. It has to be noted though that Bigger Data as such does not provide any benefits, but rather how organisations make sense of data and gain insights from analysing it. Analytic capabilities and practices are required to gain insights from Big Data, and thereby arguably improving decision-making and gaining competitive advantage. While protagonists of such Big Data Analytics (BDA) imply that those effects exist, so far they have not been confirmed by rigorous empirical research. The research questions in this thesis are: can BDA create competitive advantage, and what mechanisms drive analytic organisations to achieve competitive advantage? To explore the mechanisms, it is necessary to find out to what extent managers actually understand the implications of the analytic outputs and have capabilities and willingness to uncover and base their decisions on insights from BDA. In addition, the role of organisational culture in the context of BDA is also investigated. Data was obtained using a cross-sectional online survey which targeted Chief Information Officers and senior IT managers of medium-to-large Australian for-profit organisations. The survey yielded 163 complete responses which showed no presence of common method and non-response biases, and met the standard criteria for measurement reliability and validity. Partial least squares structural equation modelling (PLS-SEM) and multiple bootstrapping methods were used to test the hypotheses. ix

11 The empirical results verify anecdotal claims made in the literature that Big Data and related analytics do actually lead to competitive advantage, partly directly and partly indirectly. The study reveals that such benefits are achieved primarily because BDA creates additional incentives for managers to base their strategic or operational decisions on analytics, and that more analytics-based decision-making actually leads to competitive advantage. Furthermore, the results also suggest that organisational culture, in contrast to BDA tools and methods, is a valuable, rare, inimitable and nonsubstitutable resource (as it cannot be changed easily or quickly), thereby indirectly driving and sustaining competitive advantage. x